Journal of chemical information and modeling
Nov 17, 2025
Data-driven modeling based on machine learning (ML) is becoming a central component of protein engineering workflows. This perspective presents the elements necessary to develop effective, reliable, and reproducible ML models, and a set of guidelines...
Journal of computer-aided molecular design
Nov 4, 2025
Evolution has optimized proteins over time by the incorporation of precise and context-specific amino acid substitutions adapted to structural and functional demands. We have reconceptualized this principle using deep learning to engineer monoclonal ...
The booming artificial intelligence (AI) technology provides an opportunity to precisely carry out design of enzymes and create new biocatalysts with significantly enhanced performance. In the past decade, successful enzyme design cases, although t...
Journal of computer-aided molecular design
Oct 24, 2025
The interaction between staphylococcal protein A (SpA) and human immunoglobulin G (IgG) is pivotal in treating diseases such as cancer, inflammation, infections, and autoimmune disorders. However, acquiring natural SpA variants is labor-intensive, tr...
α-Helical domains are widespread and versatile, yet typically fail under low mechanical load because backbone hydrogen bonds unzip sequentially, limiting their use in force-bearing nanomaterials and molecular devices. We present an AI-guided strategy...
Rational design of signal peptides (SPs), crucial for efficient therapeutic protein secretion in Chinese hamster ovary (CHO) cells, remains challenging due to their context-dependency activity. To overcome this limitation and enable the discovery of ...
Journal of chemical information and modeling
Oct 16, 2025
The analysis and prediction of antibody-antigen (Ab-Ag) interactions often overlook critical structural features such as glycosylation and important physicochemical conditions like pH and salt concentration. Additionally, the field lacks standardized...
Proceedings of the National Academy of Sciences of the United States of America
Oct 10, 2025
While deep learning has advanced protein sequence and function design, engineering highly active and stable proteins still requires labor-intensive iterative computational design and experimentation. There is a critical need for methods capable of di...
Proceedings of the National Academy of Sciences of the United States of America
Oct 6, 2025
Deep generative models have demonstrated success in learning the protein sequence to function relationship and designing synthetic sequences with engineered functionality. We introduce the Protein Transformer Variational AutoEncoder (ProT-VAE) as an ...
Processive endoglucanases, which possess both endo- and exoglucanase activities, are considered highly promising catalysts in cellulose degradation. In this study, we employed multiple deep learning models, including MutCompute, DeepSequence, and ESM...
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